Hugging Face's logo

mayflowergmbh
/
occiglot-7b-fr-en-instruct-EXL2

Text Generation
Transformers
Safetensors
English
French
mistral
exl2
conversational
text-generation-inference
Inference Endpoints
5-bit
Model card Files Community
occiglot-7b-fr-en-instruct-EXL2 / README.md
johannhartmann's picture
johannhartmann
Upload folder using huggingface_hub
6d5d4d4 verified
preview code
|
raw
history blame
8.36 kB
metadata
language:
  - en
  - fr
license: apache-2.0
tags:
  - exl2
pipeline_tag: text-generation

image/png

Occiglot-7B-FR-EN-Instruct

A polyglot language model for the Occident.

Occiglot-7B-FR-EN-Instruct is a the instruct version of occiglot-7b-fr-en, a generative language model with 7B parameters supporting the French and English and trained by the Occiglot Research Collective. It was trained on 160M tokens of additional multilingual and code instructions. Note that the model was not safety aligned and might generate problematic outputs.

This is the first release of an ongoing open research project for multilingual language models. If you want to train a model for your own language or are working on evaluations, please contact us or join our Discord server. We are open for collaborations!

Model details

  • Instruction tuned from: occiglot-7b-fr-en
  • Model type: Causal decoder-only transformer language model
  • Languages: English, French, and code.
  • License: Apache 2.0
  • Compute resources: DFKI cluster
  • Contributors: Manuel Brack, Patrick Schramowski, Pedro Ortiz, Malte Ostendorff, Fabio Barth, Georg Rehm, Kristian Kersting
  • Research labs: Occiglot with support from SAINT and SLT
  • Contact: Discord

How to use

The model was trained using the chatml instruction template. You can use the transformers chat template feature for interaction. Since the generation relies on some randomness, we set a seed for reproducibility:

>>> from transformers import AutoTokenizer, MistralForCausalLM, set_seed
>>> tokenizer = AutoTokenizer.from_pretrained("occiglot/occiglot-7b-es-en-instruct")
>>> model = MistralForCausalLM.from_pretrained('occiglot/occiglot-7b-es-en-instruct')  # You may want to use bfloat16 and/or move to GPU here
>>> set_seed(42)
>>> messages = [
>>>    {"role": "system", 'content': 'You are a helpful assistant. Please give short and concise answers.'},
>>>    {"role": "user", "content": "qui est le président français ?"},
>>> ]
>>> tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_dict=False, return_tensors='pt',)
>>> set_seed(42)
>>> outputs = model.generate(tokenized_chat.to('cuda'), max_new_tokens=200,)
>>> tokenizer.decode(out[0][len(tokenized_chat[0]):])
'Le président français est Emmanuel Macron.'

Dataset

The training data was split evenly amongst French and English based on the total number of tokens.

English and Code

French

Training settings

  • Full instruction fine-tuning on 8xH100.
  • 0.6 - 4 training epochs (depending on dataset sampling).
  • Framework: axolotl
  • Precision: bf16
  • Optimizer: AdamW
  • Global batch size: 128 (with 8192 context length)
  • Cosine Annealing with Warmup

Tokenizer

Tokenizer is unchanged from Mistral-7B-v0.1.

Evaluation

Preliminary evaluation results can be found below. Please note that the non-English results are based on partially machine-translated datasets and English prompts (Belebele and Okapi framework) and thus should be interpreted with caution, e.g., biased towards English model performance. Currently, we are working on more suitable benchmarks for Spanish, French, German, and Italian.

Evaluation results

English

arc_challenge belebele hellaswag mmlu truthfulqa avg
occiglot/occiglot-7b-eu5 0.530717 0.726667 0.789882 0.531904 0.403678 0.59657
occiglot/occiglot-7b-eu5-instruct 0.558874 0.746667 0.799841 0.535109 0.449034 0.617905
occiglot/occiglot-7b-de-en 0.556314 0.791111 0.803824 0.568438 0.423251 0.628587
occiglot/occiglot-7b-de-en-instruct 0.604096 0.812222 0.80004 0.570574 0.493807 0.656148
LeoLM/leo-mistral-hessianai-7b 0.522184 0.736667 0.777833 0.538812 0.429248 0.600949
mistralai/Mistral-7B-v0.1 0.612628 0.844444 0.834097 0.624555 0.426201 0.668385
mistralai/Mistral-7B-Instruct-v0.2 0.637372 0.824444 0.846345 0.59201 0.668116 0.713657

French

arc_challenge_fr belebele_fr hellaswag_fr mmlu_fr truthfulqa_fr avg
occiglot/occiglot-7b-eu5 0.506416 0.675556 0.712358 0.495684 0.23507 0.525017
occiglot/occiglot-7b-eu5-instruct 0.541488 0.7 0.724245 0.499122 0.306226 0.554216
occiglot/occiglot-7b-fr-en 0.532934 0.706667 0.718891 0.51333 0.242694 0.542903
occiglot/occiglot-7b-fr-en-instruct 0.542344 0.752222 0.72553 0.52051 0.29479 0.567079
OpenLLM-France/Claire-Mistral-7B-0.1 0.486741 0.694444 0.642964 0.479566 0.271919 0.515127
mistralai/Mistral-7B-v0.1 0.525235 0.776667 0.66481 0.543121 0.280813 0.558129
mistralai/Mistral-7B-Instruct-v0.2 0.551754 0.758889 0.67916 0.506837 0.382465 0.575821

Acknowledgements

The pre-trained model training was supported by a compute grant at the 42 supercomputer which is a central component in the development of hessian AI, the AI Innovation Lab (funded by the Hessian Ministry of Higher Education, Research and the Art (HMWK) & the Hessian Ministry of the Interior, for Security and Homeland Security (HMinD)) and the AI Service Centers (funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK)). The curation of the training data is partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) through the project OpenGPT-X (project no. 68GX21007D).

License

Apache 2.0

See also